#114 Richmond (12-5)

avg: 1126.29  •  sd: 62.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
183 South Carolina-B Win 7-6 731.54 Feb 17th Commonwealth Cup Weekend 1 2024
132 Cedarville Win 7-6 1127.53 Feb 17th Commonwealth Cup Weekend 1 2024
50 Georgetown Loss 5-9 1072.21 Feb 17th Commonwealth Cup Weekend 1 2024
38 American Loss 1-13 1123.68 Feb 18th Commonwealth Cup Weekend 1 2024
100 Davenport Win 8-7 1344.44 Feb 18th Commonwealth Cup Weekend 1 2024
156 George Washington Loss 6-8 540.08 Mar 30th Atlantic Coast Open 2024
220 Dickinson** Win 13-1 873.22 Ignored Mar 30th Atlantic Coast Open 2024
168 Swarthmore Win 11-7 1244.11 Mar 30th Atlantic Coast Open 2024
125 Johns Hopkins Loss 7-9 782.84 Mar 31st Atlantic Coast Open 2024
176 Mary Washington Win 14-2 1290.11 Mar 31st Atlantic Coast Open 2024
98 Maryland Win 10-9 1359.7 Mar 31st Atlantic Coast Open 2024
204 Elon Win 11-6 953.91 Apr 13th Atlantic Coast D III Womens Conferences 2024
74 Davidson Loss 9-11 1125.92 Apr 13th Atlantic Coast D III Womens Conferences 2024
176 Mary Washington Win 9-4 1290.11 Apr 13th Atlantic Coast D III Womens Conferences 2024
163 Catholic Win 15-3 1412.68 Apr 14th Atlantic Coast D III Womens Conferences 2024
163 Catholic Win 7-6 937.68 Apr 14th Atlantic Coast D III Womens Conferences 2024
74 Davidson Win 6-5 1500.12 Apr 14th Atlantic Coast D III Womens Conferences 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)